Model organisms are a powerful research tool in molecular biology. For example, Caenorhabditis elegans (C. elegans) has increasingly been employed by researchers to study various biological processes, including cellular differentiation, neural development and function, aging, reproduction, toxicology, and genetic function. C. elegans has attracted particular research interest due to its simple anatomy, highly conserved and fully sequenced genome, amenability to various biochemical experimental methods, and fully characterized cellular anatomy.
In order to perform many types of experiments with model organisms such as C. elegans, multiple distinct sample populations of the model organism must be manipulated and examined. In many cases, these experiments involve individually processing each organism in each sample population one or more times (e.g., to sequence each organism's genome, to surgically alter each organism, to microscopically evaluate each organism's anatomy, or to assess the response of each organism following a treatment). Results obtained for the organisms in each population can then be compared. In order to extract meaningful information from these types of experiments, cross-contamination between sample populations must be avoided.
Experimental throughput and accuracy could be greatly improved by developing platforms, which can rapidly and automatically process multiple unique populations of model organisms while maintaining segregation between the populations.